Review and assessment of Boolean approaches for inference of gene regulatory networks

Pušnik, Žiga; Mraz, Miha; Zimic, Nikolaj; Moškon, Miha

doi:10.1016/j.heliyon.2022.e10222

Cited by 16 publications

(12 citation statements)

References 82 publications

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“…Moreover, recent development of computational methods for inferring Boolean network models from high-throughput data (e.g. single cell transcriptomic data) [68], [69], [70] makes it easy to develop such large- scale Boolean network models. Thus, the choice of Boolean modeling formalism in the proposed method makes it possible to scale the method to larger regulatory networks.…”

Section: Discussionmentioning

confidence: 99%

Optimizing sequential gene expression modulation for cellular reprogramming - Coupled Boolean modeling and Reinforcement Learning based method

Singh

2024

Preprint

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Regenerative medicine entails regenerating damaged cells and tissues from healthy cells through the process of cellular reprogramming. The most common method to achieve reprogramming of cells of one type to another is by modulating the activity of specific genes. However, identifying the most suitable genes for reprogramming is a challenge due to their large number in humans and their complex interactions. This study describes a computational method to predict sequential gene expression modulation for reprogramming a starting cell type to a target cell type. The proposed method integrates: (1) a Boolean model of the concerned gene regulatory network (GRN); and (2) a reinforcement learning (RL) based model for optimization. The Boolean model is used to capture the dynamic behavior of the GRN and to understand how the gene expression modulation alters its behavior. RL model is used to optimize sequential decision-making of predicting the suitable sequence of gene expression modulation. Coupling of the Boolean model and RL plays a crucial role in the proposed computational method. Boolean model captures the GRN dynamics, and thereby, constrains the combinatorially large state space. The RL model operates in this constrained state space and uses the Boolean model to evaluate the effect of modulations on GRN dynamics to predict the sequence of suitable gene expression modulations. Applicability of the proposed method is demonstrated using a toy network of 4 genes, and a biological network of heart development representing the dynamics of 15 genes.

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Section: Discussionmentioning

confidence: 99%

Optimizing sequential gene expression modulation for cellular reprogramming - Coupled Boolean modeling and Reinforcement Learning based method

Singh

2024

Preprint

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“…Compared to the other inference methods, BN inference methods offer the advantage of being parameter free and relatively easy to apply. On the other hand, they suffer from several drawbacks and limitations, arising from the discretization/binarization process and the finite space explored for inferring the regulatory processes (limited number of possible BNs), and the qualitative description of expression [ 69 ]. Nevertheless, several inference methods based on BN have been developed, such as REVEAL [ 70 ], Best-Fit [ 71 , 72 ], both available as extensions in the BoolNet R library [ 73 ], and ATEN [ 74 ].…”

Section: Inference Methodsmentioning

confidence: 99%

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Marku

Pancaldi

2023

PLoS Comput Biol

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Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

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“…GABNI extends the MIBNI algorithm by further implementing a genetic algorithm to improve the set of candidate regulators and thus the dynamic consistency of the resulting Boolean network. An evaluation of Boolean methods for GRN inference and their implementations can be found in [115] .…”

Section: Gene Regulatory Network Inferencementioning

confidence: 99%

Network-based approaches for modeling disease regulation and progression

Galindez

Sadegh

Baumbach

et al. 2023

Computational and Structural Biotechnology Journal

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Review and assessment of Boolean approaches for inference of gene regulatory networks

Cited by 16 publications

References 82 publications

Optimizing sequential gene expression modulation for cellular reprogramming - Coupled Boolean modeling and Reinforcement Learning based method

Optimizing sequential gene expression modulation for cellular reprogramming - Coupled Boolean modeling and Reinforcement Learning based method

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Network-based approaches for modeling disease regulation and progression

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